Risky Gambling Behaviors: Associations with Mental Health and a History of Adverse Childhood Experiences (ACEs)
Bibliographic record
Abstract
Abstract Problem gambling and adverse childhood experiences (ACEs) are highly co-morbid and lead to numerous adverse health outcomes. Research demonstrates that greater levels of well-being protect individuals from experiencing ACE-related harms after a history of childhood adversity; however, this relationship has not been examined in the gambling literature. We hypothesized that individuals who experienced ACEs would engage in more problem gambling behaviors. We also hypothesized that individuals who experienced ACEs and reported flourishing mental health would have lower rates of problem gambling than individuals who experienced ACEs but did not report flourishing mental health. We conducted a secondary data analysis of the adult sample in the Well-Being and Experiences (WE) Study. Examining a parent population, parents and caregivers (N = 1000; Mage = 45.2 years; 86.5% female) of adolescents were interviewed on a variety of measures, including their history of ACEs, their gambling behaviors within the past year, and their mental health and well-being. We used multinomial logistic regression analysis to examine the relationship between 15 ACEs and gambling type (i.e., non-gambler, non-problem gambler, at-risk/problem gambler). We used interaction terms between each ACE and mental health to examine the moderating role of flourishing mental health and well-being. ACEs were associated with at-risk/problem gambling supporting hypothesis 1. Contrary to hypothesis 2, overall, flourishing mental health did not moderate the relationship between ACEs and gambling severity except for one ACE. In this study, we were able to gain a better understanding of how different ACEs each contribute to varying levels of gambling severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".